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Since 1986 - Covering the Fastest Computers in the World and the People Who Run ThemTue, 03 Mar 2015 20:27:41 +0000en-UShourly1http://wordpress.org/?v=4.1.1Hardware Accelerators Are One Way to Help Wall Street, Companies Sayhttp://www.hpcwire.com/2008/09/24/hardware_accelerators_are_one_way_to_help_wall_street_companies_say/?utm_source=rss&utm_medium=rss&utm_campaign=hardware_accelerators_are_one_way_to_help_wall_street_companies_say
http://www.hpcwire.com/2008/09/24/hardware_accelerators_are_one_way_to_help_wall_street_companies_say/#commentsWed, 24 Sep 2008 07:00:00 +0000http://www.hpcwire.com/?p=6548Can hardware acceleration save Wall Street? Well, not as quickly as a multibillion-dollar bailout might, but there was plenty of discussion at this week's HPC on Wall Street conference about the advantages specialized hardware can bring to market analysts and traders. Sellers of these products were all over the place, their booths were busy, and several sessions on the subject were standing-room only.

]]>NEW YORK CITY – Can hardware acceleration save Wall Street? Well, not as quickly as a multibillion-dollar bailout might, but there was plenty of discussion at this week’s HPC on Wall Street conference about the advantages specialized hardware can bring to market analysts and traders. Sellers of these products were all over the place, their booths were busy, and several sessions on the subject were standing-room only.

The idea of separate appliances to speed up data processing has started catching on in financial services, said Geno Valente, VP of sales and marketing at XtremeData, Inc. “A few years ago, people were like, ‘Who needs that?'” The expectation was that faster and faster CPUs would yield the processing and throughput speeds needed to respond to market changes. Plus, the FPGAs that are the brains of most accelerator boards were exotic to organizations outside the scientific community and required specialized parallel-programming skills to be used effectively.

But that’s all changed, said Valente. Accelerators are now built into appliances from companies like Solace Systems and Exegy that “anyone can plug in and start using,” he said. “Libraries are being developed that make acceleration technology available to people who don’t know parallel-programming.” As a result, “Public exchanges are using accelerators. Wall Street is now taking advantage of them. And there are a lot more companies using them that we can’t talk about,” he said.

Obviously performance — faster processing of more data and more data streams — is the primary advantage vendors mention when talking about their products. Their specialized processors handle the floating-point operations that crunch the algorithms that analysts and traders do-or-die upon. “Accelerator hardware enables us to do things we couldn’t do otherwise,” said Henry Young, founder of TS-Associates, an IT services company specializing in financial middleware. “We can handle a 10-gigabit data stream through a hardware accelerator to speed up processing. You can’t compress a 10-gig stream like that with current CPU technology.”

But it’s not just speed. One reason Wall Street is getting on board is the need to score an edge over competitors and get a better handle on volatile markets. “Accelerators can differentiate a cluster,” Valente said. “Otherwise you have exactly what the other guy has. It’s accelerators and other customizable things you can add to a Linux box that give you an advantage.”

Reliability and data precision were brought up as crucial benefits of acceleration hardware during a panel discussion. Simon McIntosh-Smith, VP of applications at ClearSpeed Technology, raised the spectre of “soft errors” resulting from cosmic emissions flipping bits. ClearSpeed makes an ASIC-based accelerator board that can plug into a server or be ganged up in a rack and connected to the network. What if an alpha emission hit a processor and altered the data from “sell 10,000 shares to buy 10,000″? In that case, you’d better hope you have hardware that supports error detection as well as error correction, McIntosh-Smith said. “The point is to make sure you have a system that supports high reliability. Some hardware accelerators support it, and some don’t. Without reliability built in, you can have soft errors and not even realize it until it’s too late.”

While cosmic rays might be too sci-fi for other manufacturers, they still emphasize the high reliability features of their devices. Exegy uses reconfigurable hardware “so people can add logic to detect flipped bits or to add in more data protection as necessary,” said Scott Parsons, the company’s chief architect. And Solace Systems VP of architecture Shawn McAllister pointed out that Solace appliances can also have reliability features added. “We have firmware that monitors things and can take over in the event of a problem,” he said.

Consolidation is another benefit, these hardware makers say. Exegy’s technology is incorporated in a ticker based on reconfigurable hardware that “can handle all the North American market data feeds in one box rather than a dozen,” Parsons said, with that stream including NYSE/SIAC, NASDAQ, OPRA, and ARCA. (See it in action at marketdatapeaks.com, he noted.)

“Datacenter consolidation is very important to our customers,” McAllister said. “Some of them are in a situation where they just can’t add another server until they take one out. Accelerators can help with that.”

With appliances replacing big servers, in theory at least financial firms will save on utility bills. “We have seen lower power consumption at the system level with our FPGAs,” Parsons noted.

That would be an advantage, right?

“I thought by now we’d be hearing more customers saying they want to reduce power consumption,” McIntosh-Smith said. “But we’re not seeing that. It’s still all about speed and performance.”

And as market data grows and grows, there will probably be no end of that need. Accelerator designers admit they’re just part of the solution. Advances in middleware, algorithms, the OS stack, software development tools, will all be needed to give financial services the ability to acquire, process and interpret data faster. “We don’t see general-purpose CPUs getting that much faster,” said Parsons. “So you have to look at non-traditional approaches to all these problems.”

Meanwhile, manufacturers of other types of processors are designing their chips to meet the demands of financial applications. In June, NVIDIA, whose graphics processing units are at the heart of upscale gaming and multimedia systems (pretty demanding themselves), introduced its 240-core Tesla 10 series. The company says the chip’s teraflop of processing power can be applied to mission-critical workloads such as financial analysis. Likewise, AMD is aiming its Firestream 9250 processor at the same kind of HPC number crunching. AMD has said that developers are reporting up to a 55 times performance increase when running financial analysis code using a FireStream 9250 GPU-based accelerator versus a standalone CPU.

Regardless of one’s take on hardware accelerators, they’ve got to be considered, like any other advancement.

“If you want to play in the fast markets, you can’t ignore new technology,” said Peter Lankford of the Securities Technology Analysis Center, which evaluates and benchmarks IT for trading systems and other financial applications. “Technology budgets are going to be more constrained now, so financial companies need to pay even more attention. Things are just going to get tighter and tighter.

]]>http://www.hpcwire.com/2008/09/24/hardware_accelerators_are_one_way_to_help_wall_street_companies_say/feed/0Banks and Outsourcing: Just Say ‘Latency’http://www.hpcwire.com/2008/09/24/banks_and_outsourcing_just_say_latency/?utm_source=rss&utm_medium=rss&utm_campaign=banks_and_outsourcing_just_say_latency
http://www.hpcwire.com/2008/09/24/banks_and_outsourcing_just_say_latency/#commentsWed, 24 Sep 2008 07:00:00 +0000http://www.hpcwire.com/?p=6551A common critique of external cloud computing services is that big-time IT users, like major corporations and financial institutions, are nowhere near getting on board. That might be true for the new breed of "cloud" services, but for the financial services sector, at least, outsourcing is far from a dirty word.

A common critique of external cloud computing services is that big-time IT users, like major corporations and financial institutions, are nowhere near getting on board. That might be true for the new breed of “cloud” services, but for the financial services sector, at least, outsourcing is far from a dirty word.

Especially in the world of electronic or algorithmic trading, latency becomes a huge issue that, for some firms, can only be solved by hosting trading platforms in the same datacenters as major exchanges. For those with less-constant, less real-time demands, renting cycles on a global grid architecture, or even renting your own dedicated grid, can guarantee CPUs whenever and wherever they are needed. However they choose to do it — and even if they opt otherwise — the financial world understands the benefits, pitfalls and nuances of outsourced IT.

Ted Chamberlain, research director in the Networking & Communications Services practice at Gartner, believes we’re actually experiencing a bit of an outsourcing renaissance. Traditionally, the financial sector does not like to let technology out of its grip because they view it as such a differentiator, but “I think we’re at the point now where so many of these financial houses either are running out of space or aren’t in the locations they want to be,” says Chamberlain. “Conversely, people like BT Radianz and Savvis actually have started to, I think, build their portfolios to lure [financial institutions] away from their own datacenters.” Especially in the past 12 months, he adds, the proliferation of exchanges going online has drawn a wide range of financial institutions — from investment banks to brokerage houses — to look into hosted datacenters for the sake of interconnecting with the various sources of market data. (Customers with space in one of Savvis’ 31 international datacenters, for example, can cross-connect with anyone in any of the provider’s other locations.)

“It’s definitely becoming a larger trend,” Chamberlain forecasts. “I don’t think we’re going to see a complete flip of all these financial service companies outsourcing to these exchanges in a cloud computing model, but we do see them diversifying and putting some level of their infrastructure in certain providers.”

Although Gartner does not forecast this market, Chamberlain says his gut feeling is that these services will grow somewhere between 30-35 percent year over year, with the EMEA and APAC markets experiencing a doubling in size.

Alex Tabb, a partner in the Crisis & Continuity Services division of the Tabb Group, also sees an increase in outsourcing interest, although not across the board. The big sell-side investment banks experimented with outsourcing several years ago but many have since shied away from the practice, he says. The reason is that potential cost savings were not worth the sacrifices they had to make in terms of control, management and integration. And with such large operations, outsourcing just became too much to handle. For medium-sized banks and smaller buy-side firms, though, Tabb says hosted solutions make a lot of sense because it is easier to pay someone with that expertise than to staff an entire IT department.

Latency: Public Enemy No. 1

One area where there is no contention is the importance of low latency: It is the No. 1 reason financial firms are moving to hosted solutions. Chamberlain calls latency the primary motivator for making the move, particularly when it can be provided without “gargantuantly scaled costs.” Granted, he notes, managed services often bear a premium over simply buying a point-to-point connection or buying traditional content delivery services, but the presence of guaranteed SLAs along with the low latency make it a premium with which they can live.

Tabb says latency is at its most critical in electronic trading scenarios, when trading applications require high-speed access to the exchanges. In particular, he says, “Latency becomes a killer if you’re running VLDBs (very large databases).” An example would be a firm like Bank of New York trading billions of dollars in bonds — for such an operation, Tabb stated, latency will bring down the system. Smaller houses doing algorithmic trading don’t have quite the latency demands as their bigger counterparts because the requirements probably are not as high with 50 people accessing the trading application versus thousands, he added.

“If you’re going to outsource it, it needs to be close — physically, the proximity needs to be close. Because latency, often times, has to do with location,” says Tabb. “If you’re talking about time-sensitive applications, that becomes a deal-breaker.”

Savvis, one of the service providers Chamberlain cites as an industry leader (along with BT Radianz), sees the need for low latency driving customer demand. According to Roji Oommen, director of business development for the financial services vertical at Savvis, latency is a big deal whether firms are doing arbitrage or trying to trying to hide basket trades via division multiplexing and long trade streams. “What they found is it’s a lot cheaper to move your infrastructure inside a datacenter rather than trying to figure out how to optimize your application or get faster hardware and so forth,” he says.

With Savvis’ Proximity Hosting solution (its most popular), which places customers’ trading engines alongside the exchanges’ platforms inside Savvis’ strategically located datacenters, the latency difference compared to in-house solutions is huge, says Oommen. For example, he explains, 80 percent of the New York Stock Exchange’s equities volume occurs over BATS and Archipelago, both of which house their matching engines with Savvis. With Proximity Hosting, he says, latency goes from milliseconds to fractional microseconds. “It would be safe to say,” Oommen adds, “that more and more banks are looking at outsourced datacenter hosting, especially as they participate in markets all over the world, rather than building it in-house — particularly for automated trading.”

In Savvis’ Weehawken, N.J., datacenter, for example, customers share space with the American Stock Exchange, Philadelphia Stock Exchange, BATS Trading, FxAll/Accelor and the New York Stock Exchange.

Xasax, a Naples, Fla.-based company with space at key datacenters across the country (including Savvis Weehawken, Equinix Secaucus, Equinix Cernak and NYSE Metrotech, among others), has created an environment and solution set optimized for hosting financial software that requires access to real-time market data. According to Noah Lieske, Xasax CEO, the customers of the company’s xsProximity solution have the option of housing a trading platform 30 microseconds from NASDAQ. If customers opt to collocate in Equinix Secaucus, they are 1 millisecond from NASDAQ. Essentially, he says, Xasax’s customers want to localize trading logic next to the exchanges, and “they couldn’t do it faster because we built it out the fastest possible route — and if there’s a faster way to do it, we’ll do it.”

“For those who truly understand the low-latency game,” he adds, “it doesn’t take much for them to understand the value of putting their machines at the closest proximity to the exchange as possible.”

Other Driving Forces

Latency, cost and ease of management are not alone in driving financial services customers to outsource. Savvis’ Oommen, for example, credits a deluge of market data with bringing customers into Savvis’ fold. He says the output of options traffic in North America, for example, has increased from 20 MBps to 600 MBps. Therefore, an options trading firm could be staring down a 20x increase (from about $5,000 a month to $100,000 a month) just in raw network connectivity to handle this data load.

From Xasax’s point of view, datacenter power and space constraints also play a role in growing the business. While growth in high-frequency trading is exponential, says Lieske, the fields where Xasax’s customers and the financial institutions collocate are pretty much out of power and space. “As fast as these facilities can be built, they’re being filled up,” he says. By having space in these coveted locations, Lieske sees his company as having “a bit of a monopoly.” However, he acknowledges that limited space sometimes requires Xasax to send customers to secondary locations — a problem that is mitigated by physical cross-connects between the various datacenters. Customers can have a secondary location as their hub but still maintain the lowest possible latency between other datacenters.

In terms of cost, Lieske says a comparably equipped in-house infrastructure could cost a couple of hundred thousand dollars per month versus $20,000-$30,000 with Xasax.

For IBM, who claims a large number of financial customers for its Computing on Demand solutions, the real draw is the ability to handle peak loads without overprovisioning hardware. Christina Cunningham, a project executive on the Computing on Demand division, says IBM’s customers tend to have extreme workloads requiring lots of capacity for short periods of time. These types of jobs include risk management calculations and Monte Carlo simulations. “Why purchase something that you need to have … in your datacenter taking up space 24×7 when you might only need that in a cyclical way?” she asks rhetorically. By renting time on IBM’s global grid, customers can get resources on a dedicated, variable or dynamic basis depending on their needs. Cunningham also cites space and power constraints as a driver, noting that IBM helps customers solve these issues by offering grid resources in three strategic, minimal-latency locations: New York, Japan and London.

But Financial Firms Hate Letting Go of Their Stuff …

Stating that “[i]t is not a one-size-fits-all solution,” Tabb Group’s Tabb says there are technical, management and oversight challenges that outsourcing brings into play. Other key concerns include the effect on the bottom line; continuous capabilities — whether service providers can sustain operations in the event of a power failure or weather event; legal implications of doing computing overseas; and how much control firms require over their data. Tabb says buy-side firms often have very complex algorithms, which they hold dear. “They look at that as the mother lode; that is their firm,” he explains. “They don’t want anyone else to see it, much less touch it.” In terms of continuity, the Tabb Group often tells smaller clients that outsourced solutions offer better results than they can achieve in-house.

“It does take a certain amount of learning curve and risk tolerance to be able to take their trading systems out of their control, so to speak, and into a hosted environment,” says Xasax’s Lieske. Customers have to trust their provider is providing a high-quality environment so the customer can focus on their core competencies rather than the nuts and bolts of running a trading system, he adds. Xasax is in a good position to offer 100 percent uptime because it requires its approximately 40 partners to source things four times (it encourages six), a level of redundancy that allows for multiple failures before a customer might experience ill effects.

Oommen says Savvis has been pretty lucky in terms of having to overcome obstacles. For one, he says, the company can site its hosting of the New York Stock Exchange as a security proof point, as well as many other exchanges and every “brand-name” bank and hedge fund. For basic collocation, Oommen says customer concerns usually center around “is it staffed, is it secure [and] is it outside a nuclear blast radius of New York?”

IBM, says Cunningham, tackles security concerns by offering very secure point-to-point connections, with one datacenter featuring 26 incoming carriers for maximum redundancy. Big Blue also offers a diskless model where CPU and performance are carried out on the grid but no data is left there. However, its biggest security claim probably is IBM’s willingness to work with customers. “Most of the companies know who the manufacturers are that are really secure in the standards they want to abide by in the industry, and so we work with those types of providers to make sure we have the equipment,” Cunningham says.

Additionally, IBM has ethically hacked its infrastructure twice in the past three years “to make sure there’s no way anybody can get in”; complies with ITAR (International Traffic in Arms Regulations) for government customers; and offers “top secret and above” clearance, Cunningham says.

Gartner’s Chamberlain doesn’t see too many issues around security due to the heavy investments made by service providers and the tendency for financial firms to “kick the tires” before adopting any new technology. However, he believes too much growth in the hosting market could actually be an issue. “I don’t think we’re going to see too many instances where information was stolen [or] IP networks were sniffed. I think we’re OK there,” Chamberlain says. “I think the only future potential issue is that with the low-latency requirements, you can only do so much until the speed of light trips you up.”

If scaling gets to the point where providers cannot meet ideal latency levels, Chamberlain wouldn’t be surprised to see a customer like the Chicago Mercantile Exchange bring its systems back in-house and build out its own fiber network. However, he acknowledges, there is a lot of untapped network capacity and dark fiber in this country, so it would take a major growth spike to reach that point.

Virtualization is the Future

Nearly everyone interviewed for this story cites virtualization, in one form or another, as being a key to future growth. Oommen says Savvis has seen a big uptake in virtualization, and the company offers customers the option of running applications in a multi-tenant cloud. He cites asset management firms as a big user here, as they have many end-users who want to see their portfolios in real time — a prime job for Web servers running within the cloud. Of course, he says, there are still concerns around both security and performance along this front, and Savvis doesn’t force anyone to run in a shared environment. In terms of performance, though, he says Web services generally run smoothly on virtualized platforms.

Xasax also is growing through automation and virtualization, from storage to provisioning. One use case the company really likes is the ability to sandbox new clientele in evaluation environments, where potential customers can sign up for a VM, bring it into production, and “get the same connectivity as Credit Suisse or Bank of America,” Lieske says. And while high-frequency traders won’t mess around with a virtual OS, Lieske says they seem to have no problems with virtual storage. Virtualization also allows Xasax to offer a variety of other services, he adds, like production-quality execution management systems, FIX (Financial Information eXchange) gateways for brokers, etc. Internally, virtualization has allowed Xasax to grow with less overhead and management than would have been required in a strictly physical environment.

IBM Computing on Demand’s Cunningham says her division also is seeing an interest in virtualization from clients, particularly around virtual desktops. However, she admits, there are some hurdles to be overcome by customers before they are ready to deploy virtual desktops in real-time scenarios. “When they look at cloud, they’re really focused on server capacity that’s taking up their datacenters,” she explains. “They’re a little bit less concerned about the number of servers taking up their Exchange; what they really want is the high-performance computing, and they want to be able to send it out.” While IBM does have a virtual desktop offering for financial services, it is seeing more questions than takers at this point.

Alex Tabb also sees desktop virtualization taking off, especially among smaller firms. Markets are running 24 hours a day, he says, and traders need to access their terminals no matter where they are in the world, from any computer. “In today’s economic environment, things change on a dime: the world’s coming to an end, and all of a sudden, in 10 minutes, we’re in the big rally,” he quipped. “Having that flexibility is really important.”

Whatever Works Best

Although the credit crisis of last week was not a failure of IT, Tabb says, the turmoil does underscore the importance of finding the right trading solution, be it in-house or outsourced. And with IT spending among financial service organizations in limbo — a 180-degree turn from “definitely on the rise” six months ago — outsourcing could look even more appealing.

“A strong information technology department and strong capabilities with your human resources and your people can make all the difference in the world,” Tabb says. “Having the ability to react quickly to market changes — and here is where latency becomes a huge issue — can have a significant impact on your trades.”

]]>HPC hardware accelerators — GPUs, FPGAs, the Cell processor, and custom ASICs like the ClearSpeed floating point device — have captured the imagination of HPC users in search of higher performance and lower power consumption. While these offload engines continue to show impressive performance results for supercomputing workloads, Intel is sticking to its CPU guns to deliver HPC to the broader market. According to Richard Dracott, Intel’s general manager of the company’s High Performance Computing business unit, CPU multicore processors, and eventually manycore processors, will prevail over accelerator solutions in the financial services industry, as well as for HPC applications in general.

Dracott says he’s seen the pattern before where people get attracted to specialized hardware for particular applications. But in the end, he says, general-purpose CPUs turn out to deliver the best ROI. Dracott claims that to exploit acceleration in HPC, developers need to modify the software anyway, so they might as well modify it for multicore. “What we’re finding is that if someone is going to go to the effort of optimizing an application to take advantage of an offload engine, whatever it may be, the first thing they have to do is parallelize their code,” he told me.

To Intel’s credit, the company has developed a full-featured set of tools and libraries to help mainstream developers parallelize their codes for x86 hardware. With the six-core Dunnington in the field today and eight-core Nehalem processors just around the corner, developers will need all the help they can get to fully utilize the additional processing power.

In fact though, adding CPU-based multithreading parallelism to your app tends to be more difficult than adding data parallelism. The latter is the only type of parallelism accelerators are any good at. And if your workload can exploit data parallelism, this can be done rather straightforwardly. With the advent of NVIDIA’s CUDA, AMD’s Brook+, RapidMind’s development platform, FPGA C-based frameworks, and SDKs from ClearSpeed and other vendors, the programming of these devices has become simpler.

And it may get simpler yet. PGI compiler developer Michael Wolfe thinks there is no reason why high-level language compilers can’t take advantage of these offload engines. “We believe we can produce compilers that allow evolutionary migration from today’s processors to accelerators, and that accelerators provide the most promising path to high performance in the future,” he wrote recently in his HPCwire column.

Of course, CPUs are not standing still performance-wise. According to Dracott, when financial customers were asked how long a 10x performance advantage over a CPU-based solution would have to be maintained to make it worth their while, they told him anywhere from 2-3 years up to as much as 7 years. For production environments, the software investment required to bring accelerators into the mix needs to account for re-testing and re-certification. In the case of the financial services industry (because of regulatory and other legal requirements), this can be a significant part of the effort. “And by the time they actually make the investment in the software, the general-purpose [CPU] hardware has caught up,” says Dracott.

Maybe. A lot of applications are already realizing much better than a 10x improvements in performance with hardware acceleration. SciComp, a company that offers derivatives pricing software, recently announced a “20-100X execution speed increase” for its pricing models. Other HPC workloads have done even better. And while the CPU hardware will eventually catch up to current accelerators, all silicon is moving up the performance ladder, roughly according to Moore’s Law. So the CPU-accelerator performance gap will in all likelihood remain.

Accelerators do have a steeper hill to climb in certain areas though. Except for the Cell processor, where a PowerPC core is built-in, all accelerators require a connection to a CPU host. Depending upon the nature of the connection (PCI, HyperTransport, QuickPath, etc.) the offload engine can become starved for data because of bandwidth limitations. In fact, the time spent talking to the host can eat up any performance gains realized through faster execution. More local store on the accelerator and careful programming can often mitigate this, but the general-purpose CPU has a built-in advantage here.

Dracott points out that the lack of double precision floating point capabilities and error correction code (ECC) memory limits accelerator deployment in many HPC production environments. This is especially true in the financial space, where predictability and reliability of results are paramount. But the latest generation of offload engines all support DP to some degree, and only GPUs have an ECC problem. ClearSpeed ASICs, in particular, have full-throttle 64-bit support plus enterprise-level ECC protection. GPUs, on the other hand, will have to deal with soft error protection in some systematic way to become a more widely deployed solution for technical computing. I’ve got to believe that NVIDIA and AMD will eventually add this capability to their GPU computing offerings.

The shortcomings of accelerator solutions have prevented much real-world deployment in production situations, according to Dracott. He thinks users will continue to experiment with offload engines for several more years, but with the exception of certain application niches, most will eventually end up back at the CPU. But interest in these more exotic solutions remains high in the HPC community. HPCwire’s Dennis Barker, at this week’s High Performance on Wall Street conference, reports that the hardware accelerator companies were drawing quite a crowd and a number of FPGA-accelerated products are already on the market. “Sellers of these products were all over the place, their booths were busy, and several sessions on the subject were standing-room only,” he writes.

And despite Intel’s commitment to the x86 CPU and Dracott’s take on the future of accelerators, the company has been evolving its position on co-processor acceleration. Intel’s (and IBM’s) Geneseo initiative to extend PCI Express for offload engines and its plans to license the new QuickPath interconnect technology would seem to indicate that the company hasn’t completely discounted acceleration. AMD, of course, has Torenzza, its own co-processor integration technology. Whether Intel is just hedging its bets to counter its rival or is genuinely committed to sharing the computing world with other architectures remains to be seen.

]]>http://www.hpcwire.com/2008/09/24/intel_cpus_will_prevail_over_accelerators_in_hpc/feed/0Microsoft Aims Newest HPC Offering at Wall Streethttp://www.hpcwire.com/2008/09/23/microsoft_aims_newest_hpc_offering_at_wall_street/?utm_source=rss&utm_medium=rss&utm_campaign=microsoft_aims_newest_hpc_offering_at_wall_street
http://www.hpcwire.com/2008/09/23/microsoft_aims_newest_hpc_offering_at_wall_street/#commentsTue, 23 Sep 2008 07:00:00 +0000http://www.hpcwire.com/?p=6537When profits drop, businesses look to boost productivity and performance -- and nowhere is that demand more urgent right now than on Wall Street. Yesterday, about 60 blocks north of the scene of the recent financial meltdown, Microsoft announced it has released its latest product to provide that boost: Windows HPC Server 2008.

]]>NEW YORK CITY — When profits drop, businesses look to boost productivity and performance — and nowhere is that demand more urgent right now than on Wall Street. Yesterday, about 60 blocks north of the scene of the recent financial meltdown, Microsoft announced it has released its latest product to provide that boost: Windows HPC Server 2008. The company says the new version will give firms in the financial services business a way to easily and cost-effectively deploy scalable high performance systems.

“Companies have to be more efficient than ever with IT resources, but they need to maintain their position in a competitive marketplace,” said Bill Laing, VP of Microsoft’s Windows Server and Solutions Division, during a speech at the HPC on Wall Street conference yesterday. Financial organizations are relying more and more on high performance systems for routine but critical operations such as real-time risk analysis, Laing said, but “they require HPC solutions that deploy quickly, integrate in a heterogeneous environment, and scale from workstation to cluster.”

Built on 64-bit Windows Server, the new platform essentially puts the Windows interface on top of high-speed compute clusters. “We’re providing supercomputing to the desktop guys, the financial analysts and the ones developing models — the guys doing real-time market calculations,” said Vince Mendillo, director of Microsoft’s Server & Tools Business Group, during a briefing. “Our goal is to accelerate the time to insight.”

Microsoft worked with dozens of companies in the financial industry to get their feedback to the server system, Laing said. One such company is Lloyds TSB, one of the largest banking groups in the UK. The IT director at Lloyds TSB Corporate Markets group in London, Ricky Higgins, reported that his team was able to stand up a new 64-node cluster in a very short time. “From bare hardware to first job submitted took barely overnight,” he said. “Due to market turmoil, we need to process data much more quickly than ever before.” Higgins said processing time has been cut by about 50 percent. “We’ve been able to greatly increase the number of transactions,” he said.

To speed up processing of financial workloads, Microsoft made a series of major changes to HPC Server. Instead of using Remote Installation Service to set up a cluster, HPC Server uses Windows Deployment Service, which the company says makes scaling much faster because it uses image multicasting to deploy nodes in parallel, but it’s also easier because a wizard system guides administrators through node configuration. A “to-do list” page walks the admin through the steps needed to configure a cluster, such as defining the network topology and setting up automatic deployment.

In fact, Microsoft lists simplified administration as one of the key benefits of the new platform. “Our goal was to provide efficient, scalable management tools for setting up and deploying a cluster. Reporting capabilities are very easy to use,” Mendillo said. Along with reporting, monitoring and diagnostic tools are all built into the new management console. A good example is the Heat Map, which gives an at-a-glance look at the health of the cluster.

Remote Direct Memory Access enables process-to-process communications, so “there’s very low latency when sending a process from one machine to another,” said Mendillo. Processes can write directly to the address space in another machine. The new job scheduler has been upgraded to work better with large clusters, handle more simultaneous chores, and be used in service-oriented applications, he said. The software now follows the Open Grid Forum’s HPC Basic Profile interface for interoperability with other schedulers.

Reliability is strengthened with advanced failover capabilities. “Redundancy on head nodes guarantees that the cluster will keep running, and job scheduler clients won’t see any change in the head node during the failover process,” explained Mendillo.

Microsoft has been working with independent software developers to build scalable applications for risk analysis, modeling trading, and other financial operations, as well as compilers, debuggers, performance optimizers, libraries, and other essential tools. Through integration with Visual Studio 2008, with its parallel programming environment, the new platform makes it easier to build software that takes advantage of distributed processing power, explained Medillo.

It’s not as if HPC is new in the financial district. Like Geno Valente, a VP for database analytics device developer XtremeData, said during a session yesterday, “Most people on Wall Street have some kind of HPC in the back room.”

But that system out back is often a Linux cluster. If Microsoft can’t replace that, it’ll work with it. “Interoperability with Linux systems, mixed cluster support, are essential,” Mendillo stated. “All the major file system vendors are supporting us, and our management package will allow administrators to manage Windows and Linux systems from a single console.”

Although its focus at this time of accelerated jitters is on the financial services segment, Microsoft is looking to bring high performance clusters to other industries with HPC Server. “We’re making high-end computing affordable, increasing productivity, and making it easier for companies to scale up to meet demand,” Laing said. “HPC Server will bring high performance to a much wider audience. It will eventually make it mainstream.”

]]>http://www.hpcwire.com/2008/09/23/microsoft_aims_newest_hpc_offering_at_wall_street/feed/0The Quantitative Models Tanked Toohttp://www.hpcwire.com/2008/09/23/the_quantitative_models_tanked_too/?utm_source=rss&utm_medium=rss&utm_campaign=the_quantitative_models_tanked_too
http://www.hpcwire.com/2008/09/23/the_quantitative_models_tanked_too/#commentsTue, 23 Sep 2008 07:00:00 +0000http://www.hpcwire.com/?p=6561The confluence of the U.S. financial meltdown and this week's High Performance on Wall Street conference in New York might be one of those coincidences that's trying to tell us something.

]]>The confluence of the U.S. financial meltdown and this week’s High Performance on Wall Street conference in New York might be one of those coincidences that’s trying to tell us something. To be honest, I’m not a big believer in cosmic happenstance, but in this case it made me wonder if the financial software models had anything to do with our current economic chaos. I didn’t have to look very hard to find some correlation.

A great post by Saul Hansell at the New York Times explained why many of the risk models developed by quants didn’t see the brick wall at the end of the tunnel (see How Wall Street Lied to Its Computers). According to Hansell, there were multiple points of failure at these firms, but in many cases the quantitative models themselves hid the risks they were supposed to be revealing. Writes Hansell:

Ultimately, the people who ran the firms must take responsibility, but it wasn’t quite that simple. In fact, most Wall Street computer models radically underestimated the risk of the complex mortgage securities, they said. That is partly because the level of financial distress is “the equivalent of the 100-year flood,” in the words of Leslie Rahl, the president of Capital Market Risk Advisors, a consulting firm. But she and others say there is more to it: The people who ran the financial firms chose to program their risk-management systems with overly optimistic assumptions and to feed them oversimplified data. This kept them from sounding the alarm early enough.

That sentiment reflects a recent conversation I had with Jerry Hanweck, of Hanweck Associates, a firm that develops quantitative finance products. He told me some of the high profile hedge funds that lost a lot of money last year were also relying on limited historical data to drive their models. Especially in high frequency trading and arbitrage trading situations, Hanweck thinks the traders often misapply their statistics. According to him, when you gather all this random data together and run regression analysis on it, some of the results are going to look reasonable, just by chance. “If you try to extract too much from the limited amount of data that we have available to us, you really can overfit the data,” he explains.

In some cases though, the inverse problem occurred. Hansell writes that some models were designed to dilute the risk by looking too far back — into the last several years of trading history versus just the last several months — when things were starting to get dicey. This hid short-term volatility behind a mask of long-term stability. But to keep profits flowing, Wall Street execs had a vested interest (literally) to keep these less-than-stellar models humming along.

Many economists think that the 2007 credit crunch that launched the current downward financial spiral was set in motion by the now notorious collateralized debt obligations or CDOs. These instruments had become infested with devalued subprime loans, and at some point it became clear to investors that the risk associated with CDOs was a lot larger than originally thought.

According to Hanweck, because of the complexity of CDOs, the risk of these instruments is based on simplified assumptions. In some cases, limits in computational power made these simplifications necessary so that the valuation models could be run. “That’s what really started the problems last year and even back in 2005, when GM and Ford had their first batch of hiccups,” he says. The nature of these CDOs suggests that the buyers — investment banks, commercial banks, insurance companies, and other institutions — were engaging in faith-based capitalism.

And what about the subprime mortgages that started it all? Well, devising and selling these packages didn’t have much to do with computers or quantitative models. Says Hanweck: “That was just plain old greed.”

]]>http://www.hpcwire.com/2008/09/23/the_quantitative_models_tanked_too/feed/0Accelerating Financial Computations on Multicore and Manycore Processorshttp://www.hpcwire.com/2008/09/22/accelerating_financial_computations_on_multicore_and_manycore_processors/?utm_source=rss&utm_medium=rss&utm_campaign=accelerating_financial_computations_on_multicore_and_manycore_processors
http://www.hpcwire.com/2008/09/22/accelerating_financial_computations_on_multicore_and_manycore_processors/#commentsMon, 22 Sep 2008 07:00:00 +0000http://www.hpcwire.com/?p=6540Multicore processors promise improved computational efficiency, but achieving high efficiency execution on these processors is non-trivial. Moreover, in the financial industry, time is literally money, so high-productivity software development is just as important as efficient execution.

]]>High-performance computation is a necessity in modern finance. In general, the current value of a financial instrument, such as a stock option, can only be estimated through a complex mathematical simulation that weighs the probability of a range of future possible scenarios. Computing the value at risk in a portfolio of such instruments requires running a large number of such simulations, and optimizing a portfolio to maximize return or minimize risk requires even more computation. Finally, these computations need to be run continuously to keep up with constantly changing market data.

Although a large amount of computation is a necessity, doing it efficiently is crucial since financial datacenters are under severe power and cooling constraints. Multicore processors promise improved computational efficiency within a fixed power and cooling budget. However, achieving high efficiency execution on these processors is non-trivial. In the case of finance, new algorithms are constantly being developed by application specialists called quantitative analysts (or “quants”). Time is literally money in finance, and so high-productivity software development is just as important as efficient execution.

In this article, we will discuss high-productivity strategies for developing efficient financial algorithms that can take advantage of multicore processors, including standard x86 processors but also manycore processors such as GPUs and the Cell BE processor. These strategies can lead to one and even two orders of magnitude improvement in performance per processor.

Multicore processors allow for higher performance at the same power level by supporting multiple lightweight processing elements or “cores” per processor chip. Scaling performance by increasing the clock speed of a single processor is inefficient since the power consumed is proportional to (at least) the square of the clock rate. At some point, it is not practical to increase the clock rate further, as the power consumption and cooling requirements would be excessive. The air-cooling limit in particular was reached several years ago, and clock rates are now on a plateau. In fact, clock rates on individual cores have been decreasing slightly as processor vendors have backed away from the ragged edge in order to improve power efficiency. However, achievable transistor density is still increasing exponentially, following Moore’s Law. This is now translating into an exponentially growing number of cores on each processor chip.

Processors from Intel and AMD supporting the x86 instruction set are now available with four cores, but six and eight core processors are expected soon. Manycore processors such as GPUs and the Cell BE can support significantly more cores, from eight to more than sixteen. In addition, in modern multicore processors each core also supports vector processing, where one instruction can operate on a short array (vector) of data. This is another efficient way to increase performance via parallelism. Vector lengths can vary significantly, with current x86 processors and the Cell BE supporting four-way vectors and GPUs supporting anywhere from five to thirty-two. Vector lengths are also set to increase significantly on x86 processors, with the upcoming Intel AVX instruction set supporting 8-way vectors and the Intel Larrabee architecture supporting 16-way vectors.

Developing software for multicore vectorized processors requires fine-grained parallel programming. A fine-grained approach is needed because the product of the number of cores and the vector length in each core, which defines the number of numerical computations that can be performed in each clock cycle, can easily be in the hundreds. The other difference between modern multicore processors and past multi-processor parallel computers is that all the cores on a multicore processor must share a finite off-chip bandwidth. In order to achieve significant scalability on multicore processors, optimizing the use of this limited resource is absolutely necessary. In fact, in order to hide the latency of memory access it may be necessary to expose and exploit even more algorithmic parallelism, so one part of a computation can proceed while another is waiting for data.

The financial community has significant experience with parallel computing in the form of MPI and other cluster workload distribution frameworks. However, MPI in particular is too heavyweight for the lightweight processing elements in multicore processors (not to mention manycore processors) and cannot, by itself, optimize memory usage or take advantage of the performance opportunities made available through vectorization. Some alternative strategies are needed to get the maximum performance out of multicore processors.

We will now discuss financial workloads. Option pricing is one of the most fundamental operations in financial analytics workloads. More generally, the current value of an “instrument,” of which an option is one example, needs to be evaluated through probabilistic forecasting.

Monte Carlo methods are often used to estimate the current value of such instruments in the face of uncertainty. In a Monte Carlo simulation, random numbers are used to generate a large set of future scenarios. Each instrument can then be priced under each given future scenario, the value discounted back to the current time using an interest calculation (made complicated by the fact that interest rates can also vary with time), and the results averaged (weighted by the probability of the scenario) to estimate the current value.

Simple versions of Monte Carlo seem to be trivially parallelizable, since each simulation can run independently of any other. However, even “simple” Monte Carlo simulations have complications. First, high-quality random numbers need to be generated and we must ensure that each batch of parallel work gets a unique set of independent, high-quality random numbers. This is harder than it sounds. The currently accepted pseudo-random number generators such as Mersenne Twister are intrinsically sequential algorithms, and may involve hundreds of bytes of state.

Typically a lookup table of starting states needs to be generated so that the random number sequence can be restarted at different points in a parallel computation. Since restarting the state of a random number generator is significantly more expensive than stepping serially to the next value, in practice the parallelism is done over “batches” of Monte Carlo experiments, with each batch using a serial subsequence of the random number generator’s output. The size of the batch should be tuned to match the amount of local memory and number of cores in the processor. Also, despite the name, random number generators need to be deterministic and repeatable. For various reasons (including validation, legal and institutional), pricing algorithms need to give the same answer every time they run. Given these issues, some infrastructure that supports parallel random number generation in a consistent way is essential.

The last step in Monte Carlo algorithms can also be troublesome: averaging. First, high precision is often needed here. In practice, the results of millions of Monte Carlo experiments need to be combined. Unfortunately, sum of more than a million numbers cannot easily be done reliably using only single precision, since single precision numbers themselves only have about six to seven digits of precision. Fortunately, manycore accelerators have recently added double-precision capabilities. Second, different strategies for doing the summation, a form of what is often called “reduction,” are possible by exploiting the associativity of the addition operation. There is no single strategy of parallelism for reduction that is optimal for all processors. As with random number generation, in order to make an implementation portable it is useful if reduction operations are abstracted and done by a parallel runtime platform or framework.

Not all Monte Carlo simulations are “simple.” More sophisticated examples manipulate data structures to allow the reuse of results, or use “particle filters” to iteratively focus computation on more important parts of the search space in order to improve accuracy. Simple Monte Carlo simulations often scale very well because they use relatively little memory bandwidth. More sophisticated versions that reuse results via data structures may not scale as well unless care is taken to ensure that memory access does not become a bottleneck. Reuse of results and theoretical improvements in convergence rates need to be weighed against the reduced efficiency of more complex algorithms. However, with some care taken to ensure that the data locality present in a complex algorithm is properly exploited, good scalability is possible even for algorithms with a lot of data reuse and communication.

In order to achieve significant performance improvement on multicore processors, two things are needed: efficient use of low-level operations such as vector instructions, and second, an appropriate choice of parallelization and data decomposition strategy. The latter is obviously important, but how can it be achieved without interfering with the former, or vice-versa? The solution is to use a meta-strategy based on code generation. The dataflow pattern gives the decomposition strategy, and this is managed by one level of abstraction. After the computation has been laid out, it can be optimized for a particular set of low-level operations using a second stage of compilation.

Fortunately, good decomposition strategies can be designed for a relatively small number of recurring patterns. We’d like to figure out how to implement these patterns once, encapsulate them, and then reuse them for all occurrences of the pattern. The trick is to abstract the strategies for dealing with these patterns without introducing additional runtime overhead. Staged code generation accomplishes this. First, a high-level program serves as scaffolding for describing the dataflow of the computation, but is not involved in the actual execution. Instead, the scaffolding only serves to collect the computation into components and organize it for vectorization. Once each component is collected, a second stage of code generation can be used to perform low-level optimizations. This strategy is simpler to implement than it sounds, given the support of a suitable software development platform.

Multicore and manycore processors provide many opportunities for increased performance and greater efficiency. However, actually obtaining good scalability on any multicore processor requires both a fine-grained parallelization strategy and a dataflow design that optimizes memory usage. Memory bandwidth in particular is a limiting resource in multicore processors. Using a high-level framework, it is possible to abstract patterns of dataflow and strategies for dealing with them so they can be used efficiently, while still maintaining processor independence.

About the Authors

Dr. Michael McCool is chief scientist and co-founder of RapidMind and an associate professor at the University of Waterloo. He continues to perform research within the Computer Graphics Lab at the University of Waterloo. Professor McCool has a diverse set of published papers, and his research interests include high-quality real-time rendering, global and local illumination, hardware algorithms, parallel computing, reconfigurable computing, interval and Monte Carlo methods and applications, end-user programming and metaprogramming, image and signal processing, and sampling. He has degrees in Computer Engineering and Computer Science.

Stefanus Du Toit is chief architect and co-founder of RapidMind, and has led the development and evolution of the RapidMind platform since 2003. Stefanus has extensive experience in the areas of graphics, GPGPU, systems programming and compilers. He holds a Bachelors of Mathematics degree in Computer Science.

]]>http://www.hpcwire.com/2008/09/22/accelerating_financial_computations_on_multicore_and_manycore_processors/feed/0GPUs Finding A New Role on Wall Streethttp://www.hpcwire.com/2008/09/22/gpus_finding_a_new_role_on_wall_street/?utm_source=rss&utm_medium=rss&utm_campaign=gpus_finding_a_new_role_on_wall_street
http://www.hpcwire.com/2008/09/22/gpus_finding_a_new_role_on_wall_street/#commentsMon, 22 Sep 2008 07:00:00 +0000http://www.hpcwire.com/?p=6549Despite the carnage from this year's financial crisis, the arms race in algorithmic trading is likely to continue. Behind that competition are a variety of high performance computing technologies, such as commodity clusters, FPGA accelerators and Blue Gene supercomputers. One of the new kids on Wall Street is GPU computing, a technology that is making inroads across nearly every type of HPC application.

]]>Despite the carnage from this year’s financial crisis, the arms race in algorithmic trading is likely to continue. Behind that competition are a variety of high performance computing technologies, such as commodity clusters, FPGA accelerators and Blue Gene supercomputers. One of the new kids on Wall Street is GPU computing, a technology that is making inroads across nearly every type of HPC application. The vector processing capabilites of GPUs makes them especially well-suited to financial analytics.

A quantitative finance company that has jumped into GPU computing with both feet is Hanweck Associates LLC. The company works with institutions like brokerage firms, investment banks, and hedge funds to help them accelerate thier market data applications. Hanweck’s claim to fame is their early adoption of NVIDIA’s CUDA programming language and Tesla GPU computing platform for options analytics. The NVIDIA technology is the basis for Hanweck’s Volera product line, a financial analytics engine that is used for trading and risk management. The engine is the foundation for the company’s flagship products VoleraFeed and VoleraRisk.

Hanweck has a small team of in-house programmers that develops the software, with backgrounds ranging from the trading desk to academia. When the company started out, it was basically a quant consultancy, doing quantitative financial modeling for institutions that needed to develop debt equity valuation, market impact modeling and algorithmic trading. As they developed GPU expertise, they found a largely untapped niche for GPU middleware in financial analytics workloads.

The company has also expanded into a technology consultancy role, especially with regards to NVIDIA’s GPU computing platform. Gerald (Jerry) Hanweck, the company’s founder and principal partner, says his company has been involved in proof-of-concept project with some of the larger Wall Street firms. For example, they have a project underway to develop a mortgage analytics application for acquiring subprime mortgages. Part of the project will involve building the mortgage models around the GPU. Hanweck says they expect to realize a 100x speedup using GPUs compared to traditional CPUs. According to him, this type of experimentation is commonplace in Wall Street. He believes that most major financial institutions are exploring GPU computing at some level and many, if not all, have pilot projects in place.

While GPU performance is strongest in the single precision (32-bit) floating point, this turns out to be a good fit for financial analytics. Even though the second generation GPU computing devices will have double precision (64-bit) capability, single precision will continue to be much faster for the foreseeable future. Fortunately, you don’t need double precision for most types of numerical analysis, Hanweck explains. When 64-bit floating point became the default on CPUs, most developers just went along for the ride. “I think a lot of people got lazy over the years and took double precision for granted,” he says.

Hanweck saw the potential of the GPU acceleration in financial analytics early on, and started developing with an early version of CUDA back in February 2007. In addition to the NVIDIA technology, he also looked at FPGAs, the Cell processor and ATI’s (AMD’s) GPUs. The company even dabbled with PeakStream’s development platform (before Google bought them). According the Hanweck, nothing was as straightforward nor as well developed as NVIDIA’s CUDA-Tesla technology. And with the increasing volumes of data flowing through the financial markets and the pressure to execute trades first, Hanweck saw conventional CPU-based platforms falling behind the performance curve. “For the end user, speed is king right now,” he says.

One area where you see the data volumes overwhelming Moore’s Law CPU economics is market messaging. In the U.S. alone, there are currently about 300,000 options that trade over 3,500 stocks and indices. All the pricing data is fed into a service called OPRA — for Options Price Reporting Authority — and that data volume is taking off. “This year they expect to hit 1,000,000 messages per second,” says Hanweck. “My guess is they’ve already exceeded that.”

Hanweck remembers his stint at JPMorgan, when he was the firm’s chief equity derivatives strategist. He says in 2003 they only needed a relatively large system with conventional servers to do these options calculations. But more recently, investment banks have built much larger computing clusters or grids with many more racks of servers costing millions of dollars — and millions of dollars per year to run them. Hanweck says they can compress a system like that down to about 10U worth of rack space using NVIDIA Tesla-equipped servers.

At the datacenter of Hanweck partner ACTIV Financial Systems Inc., a couple of conventional servers are used to subscribe and publish the market data, while three NVIDIA Tesla S870-equipped servers are employed to process it. The S870 hold four 8-series GPUs, each capable of around 500 single precision gigaflops. With Hanweck’s VoleraFeed, a GPU-accelerated application that runs on top of a market feed appliance (like ACTIV’s), anytime a stock price changes, all of the options’ risks can be recomputed in under 10 milliseconds.

And that’s with the first generation GPU computing technology. When they upgrade to NVIDIA’s S1070 Tesla boards, they think they can cut that to less than 5 milliseconds. In fact, Hanweck says they’ve already tested an early version of the new device, which NVIDIA has assured them is slower than the production version. “Basically, we can cut our compute time in half just by upgrading our hardware,” says Hanweck. “It’s a lot easier to do that than to be a clever programmer.”

That statement harkens back to the 20th century experience of CPU-based computing, when applications automatically got a performance boost every time the chip vendors bumped up the processor clock speeds. With clock speeds more or less stagnant now and the promise of multicore CPU scalability still a pipe dream, the data parallelism offered by GPUs is one way at least some applications can jump back on the performance curve. The way Hanweck sees it, “from a technology standpoint, GPUs are going to change the way the world works.”

]]>http://www.hpcwire.com/2008/09/22/gpus_finding_a_new_role_on_wall_street/feed/0Beep, Beep, Ka-Chinghttp://www.hpcwire.com/2008/09/22/beep_beep_ka-ching/?utm_source=rss&utm_medium=rss&utm_campaign=beep_beep_ka-ching
http://www.hpcwire.com/2008/09/22/beep_beep_ka-ching/#commentsMon, 22 Sep 2008 07:00:00 +0000http://www.hpcwire.com/?p=6553On the eve of the annual High Performance on Wall Street conference, it's ironic that the words "high performance" and "Wall Street" can be perceived as an oxymoron this week. Is it possible, though, that the very financial crisis we're in may bode well for increased investment in HPC?

]]>I’ll admit to being a little distracted several times over the last few days as I sat down to write my blog with my Treo beeping constantly to alert me to the latest breaking news on the financial crisis. OK, maybe more than a little distracted. Admittedly, being older, and perhaps wiser, and certainly more conservative than I was in 2000, I did not feel quite as frantic as I did during the last “crisis,” having made the decision to pull out of the markets back in March when the gyrations in the Dow were already starting to keep me up at night. But I sure am worried.

On the eve of the annual High Performance on Wall Street conference (Sept. 22 at the Roosevelt Hotel in NY), it’s ironic that the words “high performance” and “Wall Street” can be perceived as an oxymoron this week. Is it possible, though, that the very financial crisis we’re in may bode well for increased investment in HPC? I sure hope so. Given the likelihood of regulatory changes for the financial services market, along with the obvious need for better risk assessment and management, higher productivity, greater cost-efficiency and more advanced global economic modeling, it seems all of the right drivers are there.

In fact, HPCwire reported in June on the results of a Microsoft survey, “High-Performance Computing Capital Markets Survey 2008,” which indicated, “capital markets firms in the last 12 months have faced increased demands to run real-time market risk analysis (25 percent), middle-office risk analytics (34 percent) and portfolio-related calculations, such as rebalancing and hedging strategies (42 percent). At the same time, Wall Street firms are turning to their growing HPC resources to assist with these activities, with companies reporting ‘a lot or some’ demand for HPC….”

Will the budgets be there? I don’t have a crystal ball, and it’s not clear how a likely reduction in IT spending by the financial services market will affect other commercial spending, let alone academia and government. As reported in Wall Street & Technology yesterday, “Matt Bienfang, senior research director of TowerGroup, has already estimated the impact that Lehman’s bankruptcy and Merrill’s pending acquisition by Bank of America will have on overall IT spending by Wall Street. ‘Between Bear, Lehman and Merrill you’re talking about 4-5 percent of the entire industry spend on technology,’ he notes. ‘All in all, we’re forecasting the ripples of this on the sell side will cause a 12-15 percent decline on overall IT spending,’ Bienfang says.”

What do you think? Will HPC weather this crisis better than IT overall? I’ll look forward to your comments here on the blog, and to seeing the results of the next Tabor Research Buyer Trends Report (email greg@taborresearch.com for more info on this), and to speaking with as many financial services execs as I can at High Performance on Wall Street next Monday. If they come.

]]>http://www.hpcwire.com/2008/09/22/beep_beep_ka-ching/feed/0Bad News on Wall Street Doesn’t Diminish the Need for Speedhttp://www.hpcwire.com/2008/09/22/bad_news_on_wall_street_doesnt_diminish_the_need_for_speed/?utm_source=rss&utm_medium=rss&utm_campaign=bad_news_on_wall_street_doesnt_diminish_the_need_for_speed
http://www.hpcwire.com/2008/09/22/bad_news_on_wall_street_doesnt_diminish_the_need_for_speed/#commentsMon, 22 Sep 2008 07:00:00 +0000http://www.hpcwire.com/?p=6558Considering the ongoing crisis in the financial markets, you might expect the mood at a gathering of people who make their living in the financial services industry to be kind of glum. Or at least very, very anxious.

]]>NEW YORK CITY – Considering the ongoing crisis in the financial markets, you might expect the mood at a gathering of people who make their living in the financial services industry to be kind of glum. Or at least very, very anxious.

Not so at today’s HPC on Wall Street conference in New York. On the local misery index, where 1 equals “Yankees lose” and 10 is “Yankees lose to Red Sox,” the mood here felt like about a 3. Of course, had this been a confab of brokers and traders, that number would probably be higher.

That’s not to say recent headlines weren’t on people’s minds. In just about every panel discussion or one-on-one conversation, there were references made to “the market chaos” or “last week’s turmoil.”

But this crowd of technology users and technology providers appeared to be thinking more about speed: Faster transactions, faster data feeds, faster analysis, faster reporting, faster applications. You just can’t get enough. As Peter Lankford, director of the Securities Technology Analysis Center (STAC), put it: “We used to trade in hundreds of milliseconds. Now we’re at the point where tens of milliseconds really matters. Requirements keep intensifying.”

It seemed like every other vendor was offering a cure for latency. Hardware to accelerate floating-point operations, messaging, storage, and the ticking of stock data was in relative abundance. Naturally the whole theme is about speeding up, but there seemed to be a proliferation of companies building accelerators out of ASICs and FPGAs. (More on that in a later report.)

Windows on Wall Street

Microsoft VP Bill Laing delivered the opening keynote to talk about how financial firms need to reduce risk and increase gains. Everyone seemed to be in agreement on this. That will require something like a rebuilding of the datacenter or at least some changes to the HPC infrastructure. Rumors that local comedian Jerry Seinfeld would show up to promote the big product of the day — Windows HPC Server 2008 — proved totally untrue, but Laing did a good job summarizing the new features and benefits, and a fellow from Lloyd’s TSB of London backed him up with real-world results. (More on that later too.) The upshot of Microsoft’s push is that HPC isn’t just right for Wall Street; it’s coming to Main Street too.

Random Bits

The software guys took some hits at several panel discussions. Apparently it’s the lack of parallel programming smarts that’s holding up progress. “We need more people who know parallelism,” said one panelist. The inability of developers to take advantage of all these cores being thrust upon us by Intel and other chip vendors was a concern voiced multiple times.

With the risky situation in the markets, risk analysis was a hot topic. One panelist said next year’s conference should be called Risk Management on Wall Street.

An ominous effect of a market downturn and upheaval in financial services was voiced by a guy from IBM’s brainiac division down in Raleigh, North Carolina: “There could be a slowdown in innovation because Wall Street drives much of the research and advances in high performance, low latency, security, high-speed networking, and so on.”

Maybe the most obvious impact of the recent Wall Street news was on the roster of speakers. As one conference organizer said, “A few speakers are missing because their companies no longer exist.”